H
Habib Zaidi
Researcher at University Medical Center Groningen
Publications - 557
Citations - 15951
Habib Zaidi is an academic researcher from University Medical Center Groningen. The author has contributed to research in topics: Imaging phantom & Correction for attenuation. The author has an hindex of 62, co-authored 513 publications receiving 13563 citations. Previous affiliations of Habib Zaidi include Johns Hopkins University & University of Southern Denmark.
Papers
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An object-oriented library incorporating efficient projection/backprojection operators for volume reconstruction in 3D PET
TL;DR: The aim of this paper is to present the development of a library of object-oriented building blocks for the implementation of both analytical and iterative reconstruction algorithms in 3D PET.
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A virtual sinogram method to reduce dental metallic implant artefacts in computed tomography-based attenuation correction for PET.
TL;DR: The proposed MAR method provides a simple and convenient approach allowing correction for the presence of metal artefacts caused by dental implants without the need to manipulate the complex raw CT data.
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Whole-body voxel-based internal dosimetry using deep learning
TL;DR: The proposed DNN-based WB internal Dosimetry exhibited comparable performance to the direct Monte Carlo approach while overcoming the limitations of conventional dosimetry techniques in nuclear medicine.
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FDG PET/CT methodology for evaluation of treatment response in lymphoma: from "graded visual analysis" and "semiquantitative SUVmax" to global disease burden assessment
TL;DR: The views on the relevance of emerging PET metrics that have the potential to replace existing ones in the near future for optimal patient management are shared.
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Artificial neural network-based system for PET volume segmentation
TL;DR: A novel application of ANNs in the wavelet domain for PET volume segmentation with promising results is presented and the proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches.